Literature DB >> 17065680

Evaluation of a new measure of blood glucose variability in diabetes.

Boris P Kovatchev1, Erik Otto, Daniel Cox, Linda Gonder-Frederick, William Clarke.   

Abstract

OBJECTIVE: Recent studies show the importance of controlling blood glucose variability in relationship to both reducing hypoglycemia and attenuating the risk for cardiovascular and behavioral complications due to hyperglycemia. It is therefore important to design variability measures that are equally predictive of low and high blood glucose excursions. RESEARCH DESIGN AND METHODS: We introduce the average daily risk range (ADRR), a variability measure computed from routine self-monitored blood glucose (SMBG) data. The ADRR was constructed using a development dataset for 39 and 31 adults with type 1 and type 2 diabetes, respectively. The formula was then fixed, and the ADRR was compared against other variability measures using an independent validation dataset containing approximately 4 months of SMBG for 254 and 81 adults with type 1 and type 2 diabetes.
RESULTS: From the 1st month of validation SMBG data, we computed the ADRR, blood glucose SD and coefficient of variation, daily blood glucose range and interquartile range, mean amplitude of glycemic excursion, M-value, and lability index. Then all measures were tested as predictors of low blood glucose (<2.2 mmol/l; <3.9 mmol/l) and high (>10 mmol/l; >22.2 mmol/l) events in the subsequent 3 months. The ADRR was the best predictor of both hypoglycemia and hyperglycemia, with a 6-fold increase in the likelihood of hypoglycemia and 3.5-fold increase in the likelihood of hyperglycemia across its risk categories.
CONCLUSIONS: In a large SMBG database, the ADRR showed strong association with subsequent out-of-control glucose readings. Compared with other variability measures, the ADRR demonstrated a superior balance of sensitivity to predicting both hypoglycemia and hyperglycemia. This prediction was independent from type of diabetes.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 17065680     DOI: 10.2337/dc06-1085

Source DB:  PubMed          Journal:  Diabetes Care        ISSN: 0149-5992            Impact factor:   19.112


  118 in total

1.  Translating glucose variability metrics into the clinic via Continuous Glucose Monitoring: a Graphical User Interface for Diabetes Evaluation (CGM-GUIDE©).

Authors:  Renata A Rawlings; Hang Shi; Lo-Hua Yuan; William Brehm; Rodica Pop-Busui; Patrick W Nelson
Journal:  Diabetes Technol Ther       Date:  2011-09-20       Impact factor: 6.118

2.  Prediction of the risk to develop diabetes-related late complications by means of the glucose pentagon model: analysis of data from the Juvenile Diabetes Research Foundation continuous glucose monitoring study.

Authors:  Andreas Thomas; Lutz Heinemann
Journal:  J Diabetes Sci Technol       Date:  2012-05-01

3.  Measures of Risk and Glucose Variability in Adults Versus Youths.

Authors:  Boris P Kovatchev
Journal:  Diabetes Technol Ther       Date:  2015-09-08       Impact factor: 6.118

4.  Are Risk Indices Derived From CGM Interchangeable With SMBG-Based Indices?

Authors:  Chiara Fabris; Stephen D Patek; Marc D Breton
Journal:  J Diabetes Sci Technol       Date:  2015-08-14

5.  Simple Linear Support Vector Machine Classifier Can Distinguish Impaired Glucose Tolerance Versus Type 2 Diabetes Using a Reduced Set of CGM-Based Glycemic Variability Indices.

Authors:  Enrico Longato; Giada Acciaroli; Andrea Facchinetti; Alberto Maran; Giovanni Sparacino
Journal:  J Diabetes Sci Technol       Date:  2019-03-31

6.  Hypoglycemia, but not glucose variability, relates to vascular function in children with type 1 diabetes.

Authors:  Alexia S Peña; Jennifer J Couper; Jennifer Harrington; Roger Gent; Jan Fairchild; Elaine Tham; Peter Baghurst
Journal:  Diabetes Technol Ther       Date:  2012-02-07       Impact factor: 6.118

7.  Pramlintide reduces the risks associated with glucose variability in type 1 diabetes.

Authors:  Boris P Kovatchev; John Crean; Anthony McCall
Journal:  Diabetes Technol Ther       Date:  2008-10       Impact factor: 6.118

8.  Insulin Pump Therapy - Influence on Body Fat Redistribution, Skeletal Muscle Mass and Ghrelin, Leptin Changes in T1D Patients.

Authors:  Dana Prídavková; Matej Samoš; Ivana Kazimierová; Ľudovít Šutarík; Soňa Fraňová; Peter Galajda; Marián Mokáň
Journal:  Obes Facts       Date:  2018-12-11       Impact factor: 3.942

9.  Assessment of Glucose Control Metrics by Discriminant Ratio.

Authors:  Vanessa Moscardó; Pau Herrero; Monika Reddy; Nathan R Hill; Pantelis Georgiou; Nick Oliver
Journal:  Diabetes Technol Ther       Date:  2020-10       Impact factor: 6.118

10.  A Review of Continuous Glucose Monitoring-Based Composite Metrics for Glycemic Control.

Authors:  Michelle Nguyen; Julia Han; Elias K Spanakis; Boris P Kovatchev; David C Klonoff
Journal:  Diabetes Technol Ther       Date:  2020-03-04       Impact factor: 6.118

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.